Using social media big data for tourist demand forecasting: A new machine learning analytical approach

Yulei Li, Zhibin Lin*, Sarah Xiao

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)
71 Downloads (Pure)


This study explores the possibility of using a machine learning approach to analysing social media big data for tourism demand forecasting. We demonstrate how to extract the main topics discussed on Twitter and calculate the mean sentiment score for each topic as the proxy of the general attitudes towards those topics, which are then used for predicting tourist arrivals. We choose Sydney, Australia as the case for testing the performance and validity of our proposed forecasting framework. The study reveals key topics discussed in social media that can be used to predict tourist arrivals in Sydney. The study has both theoretical implications for tourist behavioural research and practical implications for destination marketing.
Original languageEnglish
Pages (from-to)32-43
Number of pages12
JournalJournal of Digital Economy
Issue number1
Early online date27 Aug 2022
Publication statusPublished - 2022
Externally publishedYes

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